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SeLeP: Learning Based Semantic Prefetching for Exploratory Database Workloads

Summary: SeLeP: semantic prefetching for exploratory SQL by encoding block values and framing prefetching as a time-series forecasting problem. An encoder–decoder LSTM learns semantic (not address) access patterns, improving hit ratio up to 40% and cutting I/O ~45% (96% hit, 84% I/O reduction avg). (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13439
Venue
VLDB
Year
2024
Pagerank
4.1945683e-05
Overall Rank
11,021 | 23.33%
DOI
10.14778/3659437.3659458

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